Data Mining and Statistics for Decision Making

Data mining is the process of automatically searching large volumes
of data for models and patterns using computational techniques from
statistics, machine learning and information theory; it is the
ideal tool for such an extraction of knowledge. Data mining is
usually associated with a business or an organization's need to
identify trends and profiles, allowing, for example, retailers to
discover patterns on which to base marketing objectives.

This book looks at both classical and recent techniques of data
mining, such as clustering, discriminant analysis, logistic
regression, generalized linear models, regularized regression, PLS
regression, decision trees, neural networks, support vector
machines, Vapnik theory, naive Bayesian classifier, ensemble
learning and detection of association rules. They are discussed
along with illustrative examples throughout the book to explain the
theory of these methods, as well as their strengths and
limitations.

Key Features:

Presents a comprehensive introduction to all techniques used
in data mining and statistical learning, from classical to latest
techniques.

Starts from basic principles up to advanced concepts.

Includes many step-by-step examples with the main software (R,
SAS, IBM SPSS) as well as a thorough discussion and comparison of
those software.

Looks at a range of tools and applications, such as
association rules, web mining and text mining, with a special focus
on credit scoring.

Supported by an accompanying website hosting datasets and user
analysis.

Statisticians and business intelligence analysts, students as
well as computer science, biology, marketing and financial risk
professionals in both commercial and government organizations
across all business and industry sectors will benefit from this
book.

"Business intelligence analysts and statisticians, compliance and
financial experts in both commercial
and government organizations across all industry sectors will
benefit from this book." (Zentralblatt MATH, 2011)

Digital version available through Wiley Online Library

Instructors

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